In the most generic sense, imitation learning agents aim at mimicking the human behavior on a given task.
Given the ability to access a human expert acting in the same environment as the agent, a number of states \(\textbf {s}\)  and the corresponding actions \(\hat{\mathbf {a}}\)  are captured.
The agent is then trained to mimic the performance of the human given the captured pairs.
This learning paradigm is gaining popularity for a number of reasons.
It enables to train agents on complex tasks without requiring expert knowledge on it. Compared to reinforcement learning, where defining and shaping the reward is extremely important but often difficult to achieve, imitation learning provides a convenient solution as the reward is defined by the actions of the human expert.
This is relevant in a number of fields, such as autonomous driving or robotics [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
Moreover, cloud computing made possible to access an increasingly large corpora of expert data behavior.
An example, in the context of gaming, are the available driving simulators or board game implementations available.
Using such examples to develop AI agents is therefore compelling.
